Artificial Intelligence-assisted Drug Development

Abstract

Deep learning and machine learning algorithms, two types of artificial intelligence, have come to light as potential solutions to issues and roadblocks in the drug design and discovery process. Both in vitro and in silico techniques have the potential to significantly lower drug development costs when compared to conventional animal models. Early on in the drug research and development process, drug candidates with relevant therapeutic activities can be identified, unsuitable compounds with unwanted side effects can be excluded, and in vitro and in silico techniques can be used to limit the number of drug poisonings. Drug discovery procedures, illness modeling, target identification, artificial intelligence, drug screening, and molecular design can all be completed far more quickly and affordably than with conventional techniques.

Keywords

• Artificial Intelligence
• In silico approaches
• Target identification
• Target verification
• Target interactions.

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